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Title: Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling.

Abstract

In this report we describe an enhanced methodology for performing stochastic Bayesian inversions of atmospheric trace gas inversions that allows the time variation of model parameters to be inferred. We use measurements of methane atmospheric mixing ratio made in Livermore, California along with atmospheric transport modeling and published prior estmates of emissions to estimate the regional emissions of methane and the temporal variations in inferred bias parameters. We compute Bayesian model evidence and continuous rank probability score to optimize the model with respect to temporal resolution. Using two different emissions inventories, we perform inversions for a series of models with increasing temporal resolution in the model bias representation. We show that temporal variation in the model bias can improve the model fit and can also increase the likelihood that the parameterization is appropriate, as measured by the Baysian model evidence. .

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1569345
Report Number(s):
SAND2019-11190
679799
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Safta, Cosmin, Bambha, Ray, and Michelsen, Hope. Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling.. United States: N. p., 2019. Web. doi:10.2172/1569345.
Safta, Cosmin, Bambha, Ray, & Michelsen, Hope. Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling.. United States. doi:10.2172/1569345.
Safta, Cosmin, Bambha, Ray, and Michelsen, Hope. Sun . "Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling.". United States. doi:10.2172/1569345. https://www.osti.gov/servlets/purl/1569345.
@article{osti_1569345,
title = {Estimating Regional Methane Emissions Through Atmospheric Measurements and Inverse Modeling.},
author = {Safta, Cosmin and Bambha, Ray and Michelsen, Hope},
abstractNote = {In this report we describe an enhanced methodology for performing stochastic Bayesian inversions of atmospheric trace gas inversions that allows the time variation of model parameters to be inferred. We use measurements of methane atmospheric mixing ratio made in Livermore, California along with atmospheric transport modeling and published prior estmates of emissions to estimate the regional emissions of methane and the temporal variations in inferred bias parameters. We compute Bayesian model evidence and continuous rank probability score to optimize the model with respect to temporal resolution. Using two different emissions inventories, we perform inversions for a series of models with increasing temporal resolution in the model bias representation. We show that temporal variation in the model bias can improve the model fit and can also increase the likelihood that the parameterization is appropriate, as measured by the Baysian model evidence. .},
doi = {10.2172/1569345},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}